通过行为和声学模型对尬的多学科描述
Dajana Šipka1,2, Bogdan Vlasenko3, Maria Stein4,5
1Department of Clinical Psychology and Psychotherapy, University of Bern, Bern, Switzerland. dajana.sipka@unibe.ch.
Scientific reports
|March 21, 2025
概括
经常被忽视的尬感被使用声学分析和机器学习来研究. 更高的社会焦虑 (SA) 与更大的尬相关,机器学习模型以86.4%的准确度预测了尬状态.
科学领域:
- 心理学 心理学 心理学
- 情感科学是一种情感科学.
- 计算语言学 计算语言学
背景情况:
- 尬是一种常见的社会情绪,但与其他情绪相比,研究不足.
- 它与社会焦虑症 (SA) 有显著的重叠,这种疾病影响着相当一部分人口.
- 现有的研究还没有完全阐明尬的客观特征.
研究的目的:
- 用跨学科的方法来描述尬的情绪状态.
- 开发和验证一种行为范式,用于诱导和测量尬.
- 应用机器学习和声学分析来客观地评估情绪.
主要方法:
- 一组33名参与者描述了一个尬的经历,并大声朗读.
- 主观尬是通过自我报告尺度来衡量的.
- 客观分析涉及用于声学特征提取和情绪分类 (维度和分类) 的机器学习模型.
主要成果:
- 在参与者大声朗读他们的叙述后,主观尬评分增加了.
- 具有较高社会焦虑分数的个人报告了更大的尬.
- 机器学习模型在预测尬状态时达到86.4%的准确率;维度分析 (VAD) 显示差异化有限,而分类分析将尬置于更接近于无聊而不是悲伤的位置.
结论:
- 该研究成功地通过一种新的行为范式和声学建模来表征尬.
- 确定了尬的客观声学标志物,显示了作为社会焦虑的生物标志物的潜力.
- 这项研究为进一步研究社会情绪的客观测量提供了基础.
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